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学者姓名：赵宜升
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Abstract ：
Aiming at the problems of low data rate and limited battery power of traditional underwater wireless sensor network (UWSN) based on acoustic communication, a resource allocation strategy for magnetic induction (MI) communication-based UWSN in ocean current scenario is investigated in this paper. Specifically, multiple sensor nodes (SNs) are distributed in seawater at different depths. First, an autonomous underwater vehicle (AUV) is introduced to charge the SNs by magnetic coupling resonant wireless power transfer (MCR-WPT) technology. Then, the SNs transmit data to the AUV through MI communication. The influence of the ocean current on the SNs and AUV is analyzed. A genetic algorithm-based particle swarm optimization algorithm is used to optimize the AUV navigation trajectory. After collecting the data from the SNs, the AUV moves to the position under a surface base station and sends the collected data to the surface base station by MI communication. To minimize system energy consumption, the transmitting power of AUV and SNs are jointly optimized under the constraints of energy causality and transmitting power. The suboptimal solution to the formulated optimization problem is obtained by adopting the improved sparrow search algorithm (ISSA) combining Cauchy variation and reverse learning. Simulation results show that the ISSA has lower system energy consumption than other benchmark methods. © 2025 IEEE.
Keyword ：
Energy utilization Energy utilization Genetic algorithms Genetic algorithms Particle swarm optimization (PSO) Particle swarm optimization (PSO) Sensor nodes Sensor nodes
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| GB/T 7714 | Zhang, Tao , Zhao, Yisheng , Zhang, Shen et al. Resource Allocation Strategy for MI Communication-Based UWSN in Ocean Current Scenario [C] . 2025 . |
| MLA | Zhang, Tao et al. "Resource Allocation Strategy for MI Communication-Based UWSN in Ocean Current Scenario" . (2025) . |
| APA | Zhang, Tao , Zhao, Yisheng , Zhang, Shen , Liu, Peng , Hu, Zhiyi . Resource Allocation Strategy for MI Communication-Based UWSN in Ocean Current Scenario . (2025) . |
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Abstract ：
针对无人机存储能量有限的问题,研究了一种无人机协助边缘计算的无线供电通信网络资源分配策略.通过在地面部署激光束导向器,可以为无人机在短时间内提供足够的能量;随后,多个地面终端通过射频能量收集方式从该无人机获取能量,并将各自的计算任务卸载到配备边缘服务器的无人机.将资源分配问题建模为最优化问题.在满足能量和数据因果关系、计算资源和发射功率的约束条件下,以最小化无人机的总能耗为优化目标,通过引入帝国竞争算法,获得次优解.仿真结果表明,相比于粒子群优化算法和等上传时间分配方法,帝国竞争算法消耗的能量更少.
Keyword ：
无人机 无人机 无线供电通信网络 无线供电通信网络 资源分配 资源分配 边缘计算 边缘计算
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| GB/T 7714 | 张鑫宇 , 赵宜升 , 尤鸿艺 et al. UAV协助边缘计算的无线供电通信网络资源分配策略 [J]. | 杭州电子科技大学学报 , 2025 , 45 (3) : 50-58 . |
| MLA | 张鑫宇 et al. "UAV协助边缘计算的无线供电通信网络资源分配策略" . | 杭州电子科技大学学报 45 . 3 (2025) : 50-58 . |
| APA | 张鑫宇 , 赵宜升 , 尤鸿艺 , 梁立 , 菅凯歌 . UAV协助边缘计算的无线供电通信网络资源分配策略 . | 杭州电子科技大学学报 , 2025 , 45 (3) , 50-58 . |
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Abstract ：
Aiming at the problem of low data rate in autonomous underwater vehicle (AUV)-based underwater acoustic communication system, a hybrid magnetic induction (MI) and reconfigurable intelligent surface (RIS)-assisted communication strategy is proposed to maximize system channel capacity. Specifically, an AUV first collects data from all the seafloor nodes by adopting the MI communication technology. Then, the AUV forwards the data to another AUV carried with a RIS by using the underwater acoustic communication method. With the help of the RIS, a strong reflective path between the first AUV and a surface base station (BS) on the sea is formed. The surface BS could receive the data at a relatively high data rate. In order to maximize the system channel capacity, the acoustic incidence angle, the distance between the first AUV and the RIS, the distance between the RIS and the surface BS, acoustic signal frequency, and transmitting power are jointly optimized. The formulated optimization problem is solved by employing a butterfly optimization algorithm (BOA) and an improved butterfly optimization algorithm (IBOA), respectively. Simulation results show that the IBOA can increase the system channel capacity more effectively than the basic BOA.
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| GB/T 7714 | Hu, Zhiyi , Zhao, Yisheng , Liu, Peng et al. Hybrid MI and RIS-Assisted Acoustic Communication for Channel Capacity Maximization in AUV-Based UWAC System [J]. | 2024 IEEE 99TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2024-SPRING , 2024 . |
| MLA | Hu, Zhiyi et al. "Hybrid MI and RIS-Assisted Acoustic Communication for Channel Capacity Maximization in AUV-Based UWAC System" . | 2024 IEEE 99TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2024-SPRING (2024) . |
| APA | Hu, Zhiyi , Zhao, Yisheng , Liu, Peng , Song, Chaohua , Li, Tengteng . Hybrid MI and RIS-Assisted Acoustic Communication for Channel Capacity Maximization in AUV-Based UWAC System . | 2024 IEEE 99TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2024-SPRING , 2024 . |
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Abstract ：
The traditional underwater wireless sensor network (UWSN) based on acoustic communication has the shortcomings of low data rate and limited battery power. In this paper, hybrid acoustic and magnetic induction (MI) communication are considered to overcome the above drawbacks. A resource allocation strategy in autonomous underwater vehicle (AUV)-assisted edge computing UWSN is investigated to minimize the total system delay. Specifically, all the sensor nodes (SNs) are divided into different clusters. The SNs within a cluster send the data to the cluster head (CH) via the acoustic communication. The CH forwards the data to the AUV by the MI communication. Then, the AUV moves to the position under a surface vehicle (SV) carried with a edge server. The AUV forwards the data to the edge server through the MI communication. The transmitting power, channel bandwidth, and computational resources are jointly optimized. The formulated non-convex optimization problem is solved by using an alternating iterative optimization algorithm. Compared with other schemes, the proposed strategy can reduce the total system delay more effectively.
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| GB/T 7714 | Li, Tengteng , Zhao, Yisheng , Hu, Zhiyi et al. Resource Allocation Strategy in AUV-Assisted Edge Computing UWSN with Hybrid Acoustic and MI Communication [J]. | 2024 IEEE 99TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2024-SPRING , 2024 . |
| MLA | Li, Tengteng et al. "Resource Allocation Strategy in AUV-Assisted Edge Computing UWSN with Hybrid Acoustic and MI Communication" . | 2024 IEEE 99TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2024-SPRING (2024) . |
| APA | Li, Tengteng , Zhao, Yisheng , Hu, Zhiyi , Song, Chaohua , Liu, Peng . Resource Allocation Strategy in AUV-Assisted Edge Computing UWSN with Hybrid Acoustic and MI Communication . | 2024 IEEE 99TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2024-SPRING , 2024 . |
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Abstract ：
Aiming at the problem of limited energy stored in unmanned aerial vehicle (UAV), a resource allocation strategy for UAV-assisted edge computing in wireless powered communication networks is investigated in this paper. By deploying a laser beam director on the ground, sufficient energy can be provided for the UAV in a short period of time. Then, multiple ground terminals obtain energy from this UAV by radio frequency energy harvesting method and offload their computational tasks to the UAV with edge server. The resource allocation problem is modeled as an optimization problem. The optimization objective is to minimize the total energy consumption of the UAV subject to the constraints of energy and data causality, computational resources, and transmitting power. The suboptimal solution is obtained by introducing an imperialist competitive algorithm. Simulation results show that the imperialist competitive algorithm consumes less energy compared with the particle swarm optimization algorithm and the equal upload time allocation method.
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| GB/T 7714 | Zhang, Xinyu , Zhao, Yisheng , You, Hongyi et al. Resource Allocation Strategy for Wireless Powered Communication Networks with UAV-Assisted Edge Computing [J]. | 2024 IEEE 99TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2024-SPRING , 2024 . |
| MLA | Zhang, Xinyu et al. "Resource Allocation Strategy for Wireless Powered Communication Networks with UAV-Assisted Edge Computing" . | 2024 IEEE 99TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2024-SPRING (2024) . |
| APA | Zhang, Xinyu , Zhao, Yisheng , You, Hongyi , Jian, Kaige , Liang, Li . Resource Allocation Strategy for Wireless Powered Communication Networks with UAV-Assisted Edge Computing . | 2024 IEEE 99TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2024-SPRING , 2024 . |
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Abstract ：
Traditional underwater wireless communication in single medium has the limitations of low rate and high delay. In this paper, magnetic induction (MI)-based cross-medium communication is taken into account to reduce the transmission delay. Specifically, multiple autonomous underwater vehicles are used to collect data from underwater sensor nodes by MI communication. The collected data is directly transferred to a unmanned aerial vehicle above the water via ultra-low frequency MI communication. The cross-medium data collection and transmission problem is formulated an optimization problem. The objective is to minimize the total delay under the constraints of transmitting power, transmission distance, and number of turns of MI coil. A standard particle swarm optimization (SPSO) algorithm and a quantum-behaved particle swarm optimization (QPSO) algorithm are adopted to obtain the suboptimal solution, respectively. Simulation results show that the QPSO algorithm is superior to the SPSO algorithm in reducing the total delay.
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| GB/T 7714 | Liu, Peng , Zhao, Yisheng , Hu, Zhiyi et al. MI-Based Cross-Medium Communication for Multi-AUV-Assisted Underwater Data Acquisition [J]. | 2024 IEEE 99TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2024-SPRING , 2024 . |
| MLA | Liu, Peng et al. "MI-Based Cross-Medium Communication for Multi-AUV-Assisted Underwater Data Acquisition" . | 2024 IEEE 99TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2024-SPRING (2024) . |
| APA | Liu, Peng , Zhao, Yisheng , Hu, Zhiyi , Song, Chaohua , Li, Tengteng . MI-Based Cross-Medium Communication for Multi-AUV-Assisted Underwater Data Acquisition . | 2024 IEEE 99TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2024-SPRING , 2024 . |
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Abstract ：
The traditional underwater wireless sensor network (UWSN) based on acoustic communication has the shortcomings of low data rate and limited battery power. In this paper, hybrid acoustic and magnetic induction (MI) communication are considered to overcome the above drawbacks. A resource allocation strategy in autonomous underwater vehicle (AUV)-assisted edge computing UWSN is investigated to minimize the total system delay. Specifically, all the sensor nodes (SNs) are divided into different clusters. The SNs within a cluster send the data to the cluster head (CH) via the acoustic communication. The CH forwards the data to the AUV by the MI communication. Then, the AUV moves to the position under a surface vehicle (SV) carried with a edge server. The AUV forwards the data to the edge server through the MI communication. The transmitting power, channel bandwidth, and computational resources are jointly optimized. The formulated non-convex optimization problem is solved by using an alternating iterative optimization algorithm. Compared with other schemes, the proposed strategy can reduce the total system delay more effectively. © 2024 IEEE.
Keyword ：
Autonomous underwater vehicles Autonomous underwater vehicles Bandwidth Bandwidth Convex optimization Convex optimization Geophysical prospecting Geophysical prospecting Hybrid vehicles Hybrid vehicles Linear programming Linear programming Magnetic levitation vehicles Magnetic levitation vehicles Nonlinear programming Nonlinear programming Sensor nodes Sensor nodes
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| GB/T 7714 | Li, Tengteng , Zhao, Yisheng , Hu, Zhiyi et al. Resource Allocation Strategy in AUV-Assisted Edge Computing UWSN with Hybrid Acoustic and MI Communication [C] . 2024 . |
| MLA | Li, Tengteng et al. "Resource Allocation Strategy in AUV-Assisted Edge Computing UWSN with Hybrid Acoustic and MI Communication" . (2024) . |
| APA | Li, Tengteng , Zhao, Yisheng , Hu, Zhiyi , Song, Chaohua , Liu, Peng . Resource Allocation Strategy in AUV-Assisted Edge Computing UWSN with Hybrid Acoustic and MI Communication . (2024) . |
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Version ：
Abstract ：
Aiming at the problem of limited energy stored in unmanned aerial vehicle (UAV), a resource allocation strategy for UAV-assisted edge computing in wireless powered communication networks is investigated in this paper. By deploying a laser beam director on the ground, sufficient energy can be provided for the UAV in a short period of time. Then, multiple ground terminals obtain energy from this UAV by radio frequency energy harvesting method and offload their computational tasks to the UAV with edge server. The resource allocation problem is modeled as an optimization problem. The optimization objective is to minimize the total energy consumption of the UAV subject to the constraints of energy and data causality, computational resources, and transmitting power. The suboptimal solution is obtained by introducing an imperialist competitive algorithm. Simulation results show that the imperialist competitive algorithm consumes less energy compared with the particle swarm optimization algorithm and the equal upload time allocation method. © 2024 IEEE.
Keyword ：
Aircraft communication Aircraft communication Edge computing Edge computing Energy utilization Energy utilization Particle swarm optimization (PSO) Particle swarm optimization (PSO) Resource allocation Resource allocation Unmanned aerial vehicles (UAV) Unmanned aerial vehicles (UAV)
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| GB/T 7714 | Zhang, Xinyu , Zhao, Yisheng , You, Hongyi et al. Resource Allocation Strategy for Wireless Powered Communication Networks with UAV-Assisted Edge Computing [C] . 2024 . |
| MLA | Zhang, Xinyu et al. "Resource Allocation Strategy for Wireless Powered Communication Networks with UAV-Assisted Edge Computing" . (2024) . |
| APA | Zhang, Xinyu , Zhao, Yisheng , You, Hongyi , Jian, Kaige , Liang, Li . Resource Allocation Strategy for Wireless Powered Communication Networks with UAV-Assisted Edge Computing . (2024) . |
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Version ：
Abstract ：
Traditional underwater wireless communication in single medium has the limitations of low rate and high delay. In this paper, magnetic induction (MI)-based cross-medium communication is taken into account to reduce the transmission delay. Specifically, multiple autonomous underwater vehicles are used to collect data from underwater sensor nodes by MI communication. The collected data is directly transferred to a unmanned aerial vehicle above the water via ultra-low frequency MI communication. The cross-medium data collection and transmission problem is formulated an optimization problem. The objective is to minimize the total delay under the constraints of transmitting power, transmission distance, and number of turns of MI coil. A standard particle swarm optimization (SPSO) algorithm and a quantum-behaved particle swarm optimization (QPSO) algorithm are adopted to obtain the suboptimal solution, respectively. Simulation results show that the QPSO algorithm is superior to the SPSO algorithm in reducing the total delay. © 2024 IEEE.
Keyword ：
Aircraft communication Aircraft communication Autonomous underwater vehicles Autonomous underwater vehicles Inductive power transmission Inductive power transmission Magnetic levitation vehicles Magnetic levitation vehicles Particle swarm optimization (PSO) Particle swarm optimization (PSO) Sensor nodes Sensor nodes Unmanned aerial vehicles (UAV) Unmanned aerial vehicles (UAV)
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| GB/T 7714 | Liu, Peng , Zhao, Yisheng , Hu, Zhiyi et al. MI-Based Cross-Medium Communication for Multi-Auv-Assisted Underwater Data Acquisition [C] . 2024 . |
| MLA | Liu, Peng et al. "MI-Based Cross-Medium Communication for Multi-Auv-Assisted Underwater Data Acquisition" . (2024) . |
| APA | Liu, Peng , Zhao, Yisheng , Hu, Zhiyi , Song, Chaohua , Li, Tengteng . MI-Based Cross-Medium Communication for Multi-Auv-Assisted Underwater Data Acquisition . (2024) . |
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Abstract ：
Aiming at the problem of low data rate in autonomous underwater vehicle (AUV)-based underwater acoustic communication system, a hybrid magnetic induction (MI) and reconfigurable intelligent surface (RIS)-assisted communication strategy is proposed to maximize system channel capacity. Specifically, an AUV first collects data from all the seafloor nodes by adopting the MI communication technology. Then, the AUV forwards the data to another AUV carried with a RIS by using the underwater acoustic communication method. With the help of the RIS, a strong reflective path between the first AUV and a surface base station (BS) on the sea is formed. The surface BS could receive the data at a relatively high data rate. In order to maximize the system channel capacity, the acoustic incidence angle, the distance between the first AUV and the RIS, the distance between the RIS and the surface BS, acoustic signal frequency, and transmitting power are jointly optimized. The formulated optimization problem is solved by employing a butterfly optimization algorithm (BOA) and an improved butterfly optimization algorithm (IBOA), respectively. Simulation results show that the IBOA can increase the system channel capacity more effectively than the basic BOA. © 2024 IEEE.
Keyword ：
Data communication systems Data communication systems Interpolation Interpolation Linear programming Linear programming Magnetic levitation vehicles Magnetic levitation vehicles Underwater acoustics Underwater acoustics
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| GB/T 7714 | Hu, Zhiyi , Zhao, Yisheng , Liu, Peng et al. Hybrid MI and RIS-Assisted Acoustic Communication for Channel Capacity Maximization in AUV-Based UWAC System [C] . 2024 . |
| MLA | Hu, Zhiyi et al. "Hybrid MI and RIS-Assisted Acoustic Communication for Channel Capacity Maximization in AUV-Based UWAC System" . (2024) . |
| APA | Hu, Zhiyi , Zhao, Yisheng , Liu, Peng , Song, Chaohua , Li, Tengteng . Hybrid MI and RIS-Assisted Acoustic Communication for Channel Capacity Maximization in AUV-Based UWAC System . (2024) . |
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